Discovery-based analysis for chromatographic trends using alteration analysis (ALA) and two-dimensional correlation analysis (2DCOR)

Significance of the Topic

Advances in chromatographic chemometrics are essential to extract meaningful chemical trends from increasingly complex datasets. As modern gas chromatography and mass spectrometry platforms produce billions of data points per sample, traditional visual inspection and manual peak picking have become impractical. Discovery-driven approaches like alteration analysis (ALA) and two-dimensional correlation analysis (2DCOR) promise systematic, quantitative detection of subtle compositional changes across sample series, critical for fields such as material aging studies, environmental monitoring, and quality control in industrial analytics.

Objectives and Overview of the Study

This work introduces and expands two complementary chemometric techniques—ALA and 2DCOR—for uncovering chromatographic trends without prior knowledge of target analytes. The study aims to:

• Quantify how individual signals vary over sample series using ALA.
• Reveal interrelationships and order of signal changes via 2DCOR.
• Demonstrate applicability to high-dimensional one- and two-dimensional chromatography datasets, including GC×GC and high-resolution MS.

Methodology and Instrumentation Used

Samples were analyzed by full-scan GC-TOFMS and comprehensive GC×GC-TOFMS/HRMS, generating up to tens of billions of data points per run. ALA workflows compute three maps:

• Basic Alteration Map (BAM) quantifies overall signal range.
• Synchronous Alteration Map (SAM) captures linear trends.
• Asynchronous Alteration Map (AAM) highlights non-linear changes.

Raw chromatograms are first normalized, tiled to manage data size, and then subjected to ALA parameter calculations and signal-to-noise filtering. For 2DCOR, dynamic spectra are constructed by referencing a baseline chromatogram under controlled perturbations (e.g., time or temperature), and synchronous/asynchronous correlation matrices are generated following Noda’s formalism. A refined pipeline for GC×GC integrates ALA hit lists with 2DCOR by collapsing two-dimensional data into pseudo-one-dimensional profiles on selected m/z channels.

Main Results and Discussion

The combined ALA and 2DCOR approach successfully detected more than 250 chemical alterations in aging high-explosive samples, including low-abundance decomposition products that escaped conventional multivariate methods. Simulated and real datasets with overlapping peaks demonstrated ALA’s capability to resolve trends down to chromatographic resolutions (Rs) as low as 0.01, provided sufficient signal-to-noise. 2DCOR maps elucidated the sequence of changes among multiple analytes, enabling temporal ordering of chemical transformations. Expansion to GC×GC-HRMS required tiling strategies to handle ~80 billion datapoints, but preserved sensitivity and trend fidelity. The refined GC×GC workflow produced targeted hit lists, re-centered tile calculations, and combined correlation analysis to prioritize truly significant features.

Benefits and Practical Applications of the Method

These strategies offer:

• Automated detection of important analytes from large-scale chromatograms.
• Quantitative ranking of changes to focus laboratory resources.
• Ability to deconvolute severely overlapped signals in both one- and two-dimensional separations.
• Insight into the order and relationships of chemical events in complex reaction networks.

Applications include stability testing of energetic materials, metabolomics profiling, environmental pollutant monitoring, and quality control in pharmaceutical and petrochemical industries.

Future Trends and Applications

Future developments are expected to integrate machine-learning-based alignment algorithms to streamline GC×GC data preprocessing, scalable cloud computing to manage terabyte-scale datasets, and coupling with ultra-high-resolution MS for structural elucidation of unknowns. Real-time chemometric feedback during chromatographic runs and integration with automated sample preparation will further accelerate discovery workflows. Additionally, extension of these methods to other perturbation dimensions (e.g., pH, humidity) can broaden their utility across analytical sciences.

Conclusion

Alteration analysis and two-dimensional correlation analysis together form a powerful discovery platform for identifying and contextualizing chemical changes in complex chromatographic datasets. Their successful application to GC×GC-HRMS and aging explosive studies underscores the potential to revolutionize data-rich analytical workflows by enabling efficient, unbiased trend detection and mechanistic insights.

Reference

Herman, M. J., & Freye, C. E. Expansion of Alteration Analysis and Two-Dimensional Correlation Analysis to Two-Dimensional Chromatographic Datasets (manuscript in preparation).